Abstract—Generating classification rules from data often
leads to large sets of rules that need to be pruned. A new
pre-pruning technique for rule induction is presented which
applies instance reduction before rule induction. Training
three rule classifiers on datasets that have been reduced earlier
with instance reduction methods leads to a statistically
significant lower number of generated rules, without adversely
affecting the predictive performance. The search strategies used
by the three algorithms vary in terms of both type (depth-first
or beam search) and direction (general-to-specific or
Index Terms—Rule induction, noise filtering, instance reduction.
O. M. Othman and C. H. Bryant are with the School of Computing, Science and Engineering Newton Building, the University of Salford, Greater Manchester, M5,4WT, England, UK (e-mail:O.Othman@edu.salford.ac.uk, C.H.Bryant@salford.ac.uk).
Cite: Osama M. Othman and Christopher H. Bryant, "Pruning Classification Rules with Instance Reduction Methods," International Journal of Machine Learning and Computing vol. 5, no. 3, pp. 187-191, 2015.